Riskified tackles credit card fraud

The Israeli startup has been able to grow rapidly because even if it mistakenly approves fraud, it will give the merchant a full refund.

How do you know that your company is growing rapidly? Maybe when two and a half floors in an office building is not enough for your company and you want to rent the entire building. Maybe when you go up in an elevator and the people with you in the elevator whom you don't recognize tell you that they are your employees. You can always look at the financial data, of course, and see that the number of customers is in the thousands, instead of the hundreds that you thought you had, or that your revenue grew by a triple-digit rate this year, too, just like the year before it.

The company involved, Riskified, is developing technology to prevent e-commerce fraud. The company looks like the exact image of a startup that most of us have: a group of young Tel Aviv millennials with laptops and scooters sitting around a table, laughing, and typing something on a computer. Riskified's list of customers is highly impressive: Gucci, Prada, Macy's, Sears, and Foot Locker, plus websites that Israelis like, such as Air Europa, Wish, Last Minute, Samsung, Skullcandy, and HTC, and much more.

Riskified founders CEO Eido Gal and CTO Assaf Feldman told "Globes" that in a world in which the scale of credit card fraud is growing at a dizzying pace, e-commerce websites are often forced to reject credit cards because of a slight suspicion, even if the customer is completely honest. They say that Riskified offers merchants two things: increasing the number of deals approved; and that in any deal that turns out to involve fraud, the payment will be returned in full to the merchant.

Gal: "There are many solutions in this market. When we brought ours, they told us, 'You're kidding - another solution to help merchants?' Up until then, all fraud prevention companies did was give a positive or negative recommendation for each deal. Riskified offered a charge-back guarantee - a refund for the stolen products. That's what enabled the company to break through.

"How do we do this? With two tools. The first is technological - a system based on big data and machine learning that is capable of analyzing buyers' behavior and that does a better job of detecting which people are really suspicious. The second tool is chargeback guarantee coverage for the company. The chargeback guarantee enables Riskified to both approve more deals for stories and meet its obligation to give merchants a refund if it turns out that the company mistakenly approved a deal and the merchant did not receive payment for the goods he or she supplied."

Pushing boundaries and taking more risks

The first customer was a CD store in Brooklyn, followed by stores of ever-increasing size. Riskified now has over 1,600 customers, some of them small online stores and others huge chains with thousands of branches conducting hundreds of thousands of deals a day. The cash turnover on Riskified's systems amounts to tens of billions of dollars a year - 5% of the total volume of payments in the US according to the company's estimate - and the turnover is still growing. Following Riskified's success, other companies have switched to a similar solution, but being the first to propose the idea gives Riskified an advantage.

Gal says that Riskified's technology makes it possible to increase the rate of approved transactions from 70% to 85% overnight. "This isn't just an overblown promise made to attract customers; it's a fact. The day after you put Riskified into action, your sales turnover will jump 15% and your operating cost will shrink at the same time, because you won't have to deal with the whole risk management apparatus."

The most important element in competition in the market is maintaining a fixed rate of errors. The company is investing in improvement in the ability of its technology to detect fraud; not in reducing its margin of error, but in pushing the boundaries and taking added risks in approving deals. At the same time, Gal is unwilling to disclose how many of the deals approved by the company turned out to be fraud or how much money it has had to refund to customers.

In principle, Riskified's method is simple. They collect information about all the deals taking place online, learn to recognize the buyers' behavior and MO, and let the deep learning system detect irregularities that they believe to be typical of impostors. As the database of deals grows, the system becomes more accurate, enabling Riskified to take more risks, which in turn increases revenue for merchants. This is liable to have an alarming aspect, however: the system is collecting a lot of information about us, not always with our knowledge or consent.

How do you deal with the need to protect privacy, for example the GDPR rules?

Gal: "It's very important for us to maintain privacy and understand which information is stored and about whom. We had to prepare for this and make clear what type of information we have and how we're portrayed by the merchant. In the end, however, we provide a service that tries to catch thieves and enable people to make their purchase. We're not a marketing tool that sells its information about you."

Feldman: "If someone wants to delete details about them that we hold, it's not a problem. Someone who does this is actually damaging himself or herself; if he or she is a buyer with a good record, then the fact that we know they have no fraud on their cards enables them wherever and whatever they want, even an airplane ticket to India during a trip to Thailand. If the details deleted, it's a clean slate and everything starts from scratch."

Gal: "It's an interesting question what happens if someone we have tagged as a thief wants to delete his or her details. The law was not designed with our systems in mind. We have no means of asking the regulator in Europe, 'What did you mean when you said so and so?' So we're doing the best we can."

They can go for an offering, but this is not the aim

The characteristics of Internet thieves are a subject that they like talking about at Riskified. They say that accumulating data on deals and fraud shows a lot about human behavior. For example, a bored hacker steals credit card details online, uses the card to buy a pair of Gucci socks for $1,500, and then sells them on eBay for $1,000. No one, however, wants to get a message telling him or her that their account has been billed for a Gucci purchase when they actually prefer Delta Galil socks.

Gal: "Our systems deals in security. Since thieves are constantly attacking in new ways, we have to progress together with them. We use quantities of external information we have in order to say whether a customer is real or a thief impersonating a real customer."

Feldman: "In most cases, the thief leaves some hint behind. If they are trying to use my credit card details, they have to look as much as possible like me. Since the thief isn't Israeli, however, he or she will use a proxy and leave a trail. The machine learning algorithms are able to find these small differences between the good things and the bad things. Obviously, there are errors in both directions. We insure one direction and are constantly trying to improve in order to avoid identifying someone as a thief when he or she is actually a customer who should be approved."

Gal: "We have to protect the merchant in the first stage in which he or she is trying to discover whether the card is legitimate or stolen. At the same time, the merchant wants to give a user a positive experience, i.e. supplying a product immediately or have the customer buy from the merchant online and collect the goods from the store - even have a friend pick up an iPhone purchased online an hour earlier. This is the kind of thing we enable our customers to do that they were previously unable to do. In the end, our basic assumption is that your credit details are available online. People aren't able to block this part."

Are you saying that all the credit card details of people everywhere in the world are vulnerable?

Feldman: "Maybe not the first day that you have a new card, but after you've used it for a while, at some point, it will leak from one of the places and it can be easily obtained on the Darknet."

What new capabilities are you developing now?

Feldman: "The machine learning process is what is constantly making the services more attractive. We want to see ourselves not only in risk management for payments, but as an optimizer for the entire e-commerce process. We know what's happening at every moment, and there's an entire sequence the buyer goes through before he or she makes a purchase. With a lot of data and a lot of work, I'm creating something that solves a very specific problem and can help in a lot of the stages on the way, for example protecting the login region and making the identification smarter."

Gal: "We're trying to see where we can push higher approval percentages for merchants. An example we have now is cooperation with banks in the US: we tell the bank that the buyer is all right and that they shouldn't block his or her account."

Feldman: "Six years ago, people in the know told us that it wasn't definite that we could offer the solution in a way that would be economically efficient. Two or three years later, it was already, 'OK, terrific, it's possible.'"

The impression is that the market potential is very great. What are you aiming at now?

Gal: "We'll continue growing. We want to remain independent, whether as a private or a public company. We're looking at the potential for an offering, but it's not the focus now. As far as the company's financial data are concerned, we're candidates for it in the coming year, but from the standpoint of focus and internal readiness, we're not there yet. The money is available very freely on the private market as soon as you get to a certain size."

Do you need more money?

Gal: "We'll get to a positive cash flow already in the coming year; it's very exciting and enjoyable. But we may want to strengthen the balance sheet more to provide more flexibility in case we want to make mergers and acquisitions and growth isn't organic."

What is the scale of revenue and the company value? Are you on the way to becoming a unicorn?

Gal: "We've announced that multi-year growth is 270%. The average growth each year is 3.7-fold."

Feldman: "We don't like the concept of unicorn. It's a valuation-guided concept. We wanted to create a company that gives value, and we weren't afraid to continue building a bigger and bigger company. You're making the valuation the goal and that's not our focus. Our board consists of people who believe in growth - people who let us runt with this and aren't pushing for an exit. We're running a marathon."

Working with anyone who does not work with Amazon

When Feldman and Gal talk about a marathon and rapid growth, they are also referring to growth in the number of Riskified's employees. The company currently has 260 employees, including 60 in New York, of whom 57 are new employees. The plan is to reach 350 employees by early next year and 500 in the following year. Gal currently spends most of his time in the US. "I was in Israel a month ago and I came back now, and 20 people were added to the staff during this time. It's necessary to learn to contain the growth, both physically in terms of tables and work stations on the floor and mentally. You ask yourself, 'How did we get from a place where everybody knows everybody else to a situation in which half of the company has been there less than a year, and how is it possible to create a level of interest and a connection and understanding throughout all of this?' You have to think about the construction and how people work and communicate with each other. It changes on the fly and all of these things are terribly difficult. You always feel that you don't have enough time to do everything you should and that nothing is good enough. But when you say, 'Where I'm now compared with a year ago is OK,' it's amazing."

So you actually don't know some of the people working here.

Riskified marketing director Shalhevet Zohar: "You get into the elevator with people and ask what floor they need, and they say the same floor that you need. It's getting to be embarrassing."

Gal: "I introduced myself to someone in the elevator yesterday because I was sure that she was a new employee, and then I realized that she wasn't. It goes in both directions."

Riskified's office floors are reminiscent of a kibbutz dining room. They have large work spaces with dozens of long tables and hold 200 employees, who carry their laptop with them to wherever they have decided to sit at that particular time. The tumult makes you wonder how people can concentrate at all, although the young people do not appear to be disturbed. The absence of dividers makes every employee constantly accessible for a chat or consultation, but on the other hand makes it difficult for him or her to focus on the task when necessary.

A description of the work conditions at Riskified is no trivial matter; it touches the main point. It is a sign of a trend in Israeli high tech of a group of young companies that has gotten on the fast track and is handling meteoric growth and a volume of activity that would challenge even experienced professional business managers. It also reflects the preferences of the employees themselves: while other companies are being forced to battle for each employee, it appears that employees really want to work in the company.

How does Amazon solve problems of online fraud?

Gal: "Amazon has teams of hundreds of employees, if not thousands, doing the same thing for their internal system that we're doing. We defined our market as any retailer who doesn't work with Amazon."

In the framework of your growth, is there any chance that Amazon will take an interest in you and buy you?

Gal: "It's possible, but I think that it's easier to imagine the potential size of our market without Amazon. If we do work with them. I believe that it will be in a very specific segment, maybe specific countries, maybe segments in which they have problems."

What is your advantage over them?

Gal: "We tell customers, 'If you aren't at Amazon's level of sophistication, then we're better. We enable our merchants to provide a "stunning customer experience," as Amazon describes itself. Without us, there are merchants offering delivery of a product on the same day, but when you check, it turns out that it's only after two days of 'processing the request,' during which they manage the risks."

Feldman: "We work with large companies for which we're a very non-trivial part of their effort to give Amazon a fight. These companies aren't agile in technology or learning. They have to take themselves in hand, or they'll be out of business."